本文選題：大豆油 + 反式脂肪酸��； 參考：《哈爾濱商業(yè)大學(xué)》2017年碩士論文

【摘要】：食用油是人類膳食的重要組成部分,其質(zhì)量安全對(duì)人體健康至關(guān)重要。近年來(lái),食用油脂中反式脂肪酸含量超標(biāo)問(wèn)題尤為嚴(yán)重,已引起社會(huì)各界的廣泛關(guān)注。目前,我國(guó)針對(duì)反式脂肪酸含量的測(cè)定方法普遍存在消耗化學(xué)試劑多、檢測(cè)速度慢、測(cè)定程序復(fù)雜、需要對(duì)樣品進(jìn)行復(fù)雜的前期處理等問(wèn)題,難以滿足現(xiàn)代社會(huì)對(duì)食用油質(zhì)量快速、準(zhǔn)確、簡(jiǎn)便、現(xiàn)場(chǎng)化的測(cè)定要求。太赫茲光譜分析技術(shù)可以解決傳統(tǒng)方法的諸多問(wèn)題,更適用于油脂生產(chǎn)過(guò)程中的品質(zhì)監(jiān)控,因此本文提出基于太赫茲光譜技術(shù)檢測(cè)食用油中反式脂肪酸含量的方法,以我國(guó)第一大食用油——大豆油為例,重點(diǎn)對(duì)反式脂肪酸含量太赫茲光譜分析中的數(shù)據(jù)處理以及建模方法進(jìn)行深入研究。首先,制備出不同反式脂肪酸含量的大豆油脂樣品34個(gè),利用氣相色譜儀精確測(cè)定了其反式脂肪酸含量,同步采集了樣品的太赫茲時(shí)域譜并利用傅里葉變換將其轉(zhuǎn)換為頻域譜,然后通過(guò)光學(xué)參數(shù)計(jì)算得出太赫茲吸收譜以及折射譜。在分析了大豆油脂太赫茲光譜吸收特點(diǎn)的基礎(chǔ)上,剔除異常樣品1個(gè)。將33個(gè)樣品進(jìn)行了分集,按反式脂肪酸含量的多少進(jìn)行排序,從中選取28個(gè)作為訓(xùn)練集樣品建立校正模型,剩下的5個(gè)作為預(yù)測(cè)集樣品對(duì)模型進(jìn)行驗(yàn)證;為了尋找最佳模型,分別利用偏最小二乘(PLS)、支持向量機(jī)回歸(SVR)和最小二乘支持向量機(jī)(LS-SVR)三種方法建模并對(duì)比分析,最終確定建模效果最好的是LS-SVR,預(yù)測(cè)誤差均方根RMSEP為0.3246,決定系數(shù)R2為0.9792,相對(duì)標(biāo)準(zhǔn)差RSD為3.81%,可以滿足實(shí)際檢測(cè)要求;為了進(jìn)一步提高模型的預(yù)測(cè)精度,分別采用網(wǎng)格搜索法、粒子群算法(PSO)、遺傳算法(GA)對(duì)LS-SVR模型參數(shù)進(jìn)行優(yōu)化,并對(duì)優(yōu)化結(jié)果進(jìn)行對(duì)比分析,發(fā)現(xiàn)粒子群(PSO)算法對(duì)LS-SVR模型參數(shù)優(yōu)化的效果更佳、更穩(wěn)定,預(yù)測(cè)誤差均方根(RMSEP)、決定系數(shù)(R2)和預(yù)測(cè)相對(duì)標(biāo)準(zhǔn)偏差(RSD)分別達(dá)到0.0763、0.9989和0.90%,模型的預(yù)測(cè)精度得到了顯著提高。本文研究證明了利用太赫茲光譜檢測(cè)油脂中反式脂肪酸含量的可行性,為開發(fā)專用油脂太赫茲光譜分析儀器及實(shí)現(xiàn)在線檢測(cè)奠定了理論基礎(chǔ)。
[Abstract]:Edible oil is an important part of human diet, its quality and safety is very important to human health. In recent years, the problem of trans fatty acids in edible oils is especially serious, which has attracted wide attention from all walks of life. At present, there are many problems in the determination of trans fatty acids in China, such as the consumption of chemical reagents, the slow detection speed, the complexity of the determination procedures, and the need for complex pre-treatment of the samples. It is difficult to meet the requirements of fast, accurate, simple and field determination of edible oil in modern society. Terahertz spectroscopy can solve many problems of traditional methods and is more suitable for quality control in oil production. Therefore, a method based on terahertz spectroscopy to detect trans fatty acid content in edible oil is proposed in this paper. Taking soybean oil, the largest edible oil in China, as an example, the data processing and modeling method in THz spectrum analysis of trans fatty acid content were studied in detail. First of all, 34 soybean oil samples with different trans fatty acid contents were prepared, and their trans fatty acids were accurately determined by gas chromatograph. The terahertz time-domain spectra of the samples were simultaneously collected and converted into frequency domain spectra by Fourier transform. Then the terahertz absorption spectrum and refraction spectrum are calculated by optical parameters. On the basis of analyzing the absorption characteristics of terahertz spectrum of soybean oil, one abnormal sample was eliminated. In order to find the best model, 33 samples were sorted according to the content of trans fatty acids, 28 samples were selected as training set samples to establish calibration model, and the remaining 5 samples were used as predictive set samples to verify the model. Three methods, partial least squares (PLS), support vector machine regression (SVR) and least squares support vector machine (LS-SVR), are used to model and analyze the model. Finally, LS-SVR is the best model, the root mean square (RMSEP) of prediction error is 0.3246, the coefficient of determination (R2) is 0.9792, and the relative standard deviation (RSD) is 3.81. in order to further improve the prediction accuracy of the model, the grid search method is used. Particle Swarm Optimization (PSO), genetic algorithm (GA), is used to optimize the parameters of LS-SVR model, and the results are compared and analyzed. It is found that PSO (Particle Swarm Swarm Optimization) algorithm is more effective and stable in the optimization of LS-SVR model parameters. The mean square error (RMS), the determination coefficient (R2) and the relative standard deviation (RSD) of prediction are 0.0763 ~ 0.9989 and 0.90, respectively. The prediction accuracy of the model has been improved significantly. In this paper, the feasibility of using terahertz spectrum to detect the content of trans fatty acids in oils has been proved, which lays a theoretical foundation for the development of a special THz spectrometer for oil analysis and the realization of on-line detection.
【學(xué)位授予單位】：哈爾濱商業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TS227;TP18

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